Speech analysis algorithmic system and method for objective evaluation and/or disease detection

ABSTRACT

Systems and methods use patient speech samples as inputs, use subjective multi-point ratings by speech-language pathologists of multiple perceptual dimensions of patient speech samples as further inputs, and extract laboratory-implemented features from the patient speech samples. A predictive software model learns the relationship between speech acoustics and the subjective ratings of such speech obtained from speech-language pathologists, and is configured to apply this information to evaluate new speech samples. Outputs may include objective evaluation of the plurality of perceptual dimensions for new speech samples and/or evaluation of disease onset, disease progression, or disease treatment efficacy for a condition involving dysarthria as a symptom, utilizing the new speech samples.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.15/693,699 filed on Sep. 1, 2017, which claims the benefit of U.S.Provisional Patent Application No. 62/382,494 filed on Sep. 1, 2016,wherein the disclosures of the foregoing applications are herebyincorporated by reference herein in their entireties.

GOVERNMENT RIGHTS IN INVENTION

This invention was made with government support under R21 DC012558awarded by the National Institutes of Health. The government has certainrights in the invention.

TECHNICAL FIELD

This disclosure relates to systems and methods for speech evaluationand/or analysis, and in certain aspects to evaluation of disease onset,disease progression, disease treatment efficacy and/or need fortherapeutic intervention for a condition including dysarthria as asymptom.

BACKGROUND

A speech-language pathologist (SLP) works with adults and childrenhaving difficulty with speech or processing language properly. Theirpatients may have problems related to motor and physical skills, as wellas cognitive issues affecting the ability to express language.

One type of motor speech disorder is dysarthria, which is characterizedby poor articulation of phonemes—a condition in which muscles thatproduce speech experience problems that make it difficult for a speakerto pronounce words. Neurological injury due to damage in the central orperipheral nervous system may result in weakness, paralysis, or a lackof coordination of motor components of the motor-speech system,producing dysarthria. Various potential causes of dysarthria broadlyinclude toxic conditions, metabolic conditions, degenerative diseases,traumatic brain injury, or thrombotic or embolic stroke. Specificexamples of toxic and metabolic conditions include Wilson's disease,hypoxic encephalopathy such as occurs in drowning, and central pontinemyelinolysis. Specific examples of other conditions that may lead todysarthria include brain tumors, cerebral palsy, Guillain-Barrésyndrome, hypothermia, Lyme disease, intercranial hypertension, andTay-Sachs (including late onset Tay-Sachs or LOTS) disease.

Dysarthria can affect one or more of the respiration, phonation,resonance, prosody, and articulation speech subsystems, thereby leadingto impaired intelligibility, audibility, naturalness, and efficiency ofspoken communication. Dysarthria can progress to a total loss of speech,referred to as anarthria.

Dysarthrias are classified in multiple ways based on the presentation ofsymptoms. Specific dysarthrias include spastic (resulting from bilateraldamage to the upper motor neuron), flaccid (resulting from bilateral orunilateral damage to the lower motor neuron), ataxic (resulting fromdamage to the cerebellum), unilateral upper motor neuron (presentingmilder symptoms than bilateral upper motor neuron damage), hyperkineticand hypokinetic (resulting from damage to parts of the basal ganglia,such as in Huntington's disease or Parkinsonism), and mixed dysarthrias(in which symptoms of more than one type of dysarthria are present).Individuals with dysarthria may experience challenges in one or more oftiming, vocal quality, pitch, volume, breath control, speed, strength,steadiness, range, and tone.

Speech-language pathologists are involved in diagnosis of dysarthria andtreatment of articulation problems resulting from dysarthria.

Speech-language pathologists typically use perceptual assessment to makeclinical diagnoses, severity judgments, and management decisions, and tojudge disease progression. Clinical assessments are predominantlyconducted through subjective tests performed by speech-languagepathologists (e.g. making subjective estimations of the amount of speechthat can be understood, number of words correctly understood in astandard test battery, etc.). Perceptual judgments are easy to renderand have strong face validity for characterizing speech deficits.Subjective tests, however, can be inconsistent and costly, often are notrepeatable, and subjective judgments may be highly vulnerable to bias.In particular, repeated exposure to the same test subject (e.g.,patient) over time can influence the assessment ratings generated by aspeech-language pathologist. As such, there is an inherent ambiguityabout whether the patient's intelligibility is confounded with increasedfamiliarity with the patient's speech, as both may affect subjectiveassessment by the speech-language pathologist.

Existing objective measures in speech and language clinics focus onmeasuring aspects of speech signals that are not interpretable inclinical settings. Examples of such objective measures includeinstruments that measure pitch, formants, energy, and other similarmetrics.

A need exists in the art to provide a platform to bridge thesubjective-objective divide by combining the face validity of perceptualassessment with a system providing reliable objective outcome measuresso as to affect diagnosis of dysarthrias and the standard of care formotor speech assessment.

SUMMARY

The present disclosure involves creation and use of novel speechanalysis algorithms that offer an objective measure of the subjectiveassessments typically performed by speech-language pathologists. Aprincipal objective is to offer a platform to sensitively assesstherapeutic need, disease onset, disease progression, and treatmentefficacy with unbiased, perception-calibrated metrics. Clinicallymeaningful speech quantification will for the first time provide validand reliable outcome measures, as is necessary for day-to-day clinicaldecision-making, and for the evaluation of efficacy of therapeuticinterventions. Systems and methods disclosed herein have the potentialto radically alter the standard of care for motor speech assessment, andto transform speech disorder evaluation by enabling integration intoexisting telehealth platforms for on-the-spot objective outcomemeasures.

Systems and methods disclosed herein use patient speech samples asinputs, use subjective ratings by speech-language pathologists ofpatient speech samples as further inputs, and extractlaboratory-implemented features from the patient speech samples. Thesubjective ratings may evaluate of the speech on a multi-point (e.g.,7-point) scale for five commonly assessed perceptual dimensions, namely:nasality, prosody, articulatory precision, vocal quality, and severity.A predictive software model learns the relationship between speechacoustics (embodied in the laboratory-implemented features) and thesubjective ratings of the same speech obtained from speech-languagepathologists, and is configured to apply this information to evaluatenew speech samples. Signal processing capabilities and machine learningalgorithms may be utilized to continually refine the model withincreased input, to permit algorithms of the predictive software modelto become more refined with each iteration. Thus, the output of thepredictive software model is immediately clinically transparent, anddoes not require any norms or references for comparison. Systems andmethods disclosed herein have the potential to transform speech disorderevaluation, by permitting integration into existing telehealth platformsto provide on-the-spot objective outcome measures.

In one aspect, the present disclosure relates to a method for evaluatingspeech in a system involving processor circuitry, the method comprising:selecting a subset of a plurality of laboratory-implemented featuresfrom a data matrix that includes (i) the plurality oflaboratory-implemented features, wherein said plurality oflaboratory-implemented features is extracted from a plurality of patientspeech samples, and (ii) a plurality of subjective expert ratingscorresponding to the plurality of patient speech samples and involvingevaluations on a multi-point scale for a plurality of perceptualdimensions including two or more of nasality, prosody, articulatoryprecision, vocal quality, and severity; wherein the subset of theplurality of laboratory-implemented features is relevant for predictingthe plurality of perceptual dimensions; and wherein the plurality oflaboratory-implemented features comprises an envelope modulationspectrum, a long-term average spectrum, spatio-temporal features, anddysphonia features; and utilizing the subset of the plurality oflaboratory-implemented features to generate and/or update a predictivesoftware model configured to receive at least one additional patientspeech sample and to perform at least one of the following items (a) or(b): (a) generating an objective evaluation of the plurality ofperceptual dimensions utilizing the at least one additional patientspeech sample; or (b) evaluating at least one of disease onset, diseaseprogression, or disease treatment efficacy for a condition involvingdysarthria as a symptom, utilizing the at least one additional patientspeech sample.

In certain embodiments, the method further comprises electronicallyreceiving the plurality of patient speech samples and the plurality ofsubjective expert ratings; and extracting the plurality oflaboratory-implemented features from the plurality of patient speechsamples for inclusion in the data matrix.

In certain embodiments, the method further comprises electronicallyreceiving the at least one additional patient speech sample; andgenerating an objective evaluation of the plurality of perceptualdimensions utilizing the at least one additional patient speech sample.

In certain embodiments, the method further comprises electronicallyreceiving the at least one additional patient speech sample; andevaluating at least one of disease onset, disease progression, ordisease treatment efficacy for a condition involving dysarthria as asymptom, utilizing the at least one additional patient speech sample.

In certain embodiments, the method further comprises prompting at leastone patient to read displayed text prior to, or concurrently with, theelectronic receiving of the at least one additional patient speechsample. In certain embodiments, the method further comprises providinguser-perceptible feedback to the at least one patient while the at leastone patient reads the displayed text, to alert the at least one patientto attainment of one or more conditions indicative of a speech problem.In certain embodiments, the user-perceptible feedback comprises tactilefeedback.

In certain embodiments, the plurality of perceptual dimensions includeseach of nasality, prosody, articulatory precision, vocal quality, andseverity.

In certain embodiments, the selecting of the subset of the plurality oflaboratory-implemented features comprises use of lasso or

₁-regularized regression.

In certain embodiments, the selecting of the subset of the plurality oflaboratory-implemented features comprises use of cross-validation andsparsity-based feature selection.

In certain embodiments, the selecting of the subset of the plurality oflaboratory-implemented features further comprises centering data of thesubset. In certain embodiments, the selecting of the subset of theplurality of laboratory-implemented features further comprises reducingthe subset of the plurality of laboratory-implemented features to lessthan about 40 for each dimension of the plurality of perceptualdimensions.

In another aspect, the disclosure relates to a computer programcomprising instructions which, when executed by processor circuitryincluding at least one processor, cause the at least one processor tocarry out the method as disclosed herein.

In another aspect, the disclosure relates to a system for evaluatingspeech, the system comprising: at least one memory configured to store adata matrix including (i) a plurality of laboratory-implemented featuresextracted from a plurality of patient speech samples and (ii) aplurality of subjective expert ratings corresponding to the plurality ofpatient speech samples and involving evaluations on a multi-point scalefor a plurality of perceptual dimensions including two or more ofnasality, prosody, articulatory precision, vocal quality, and severity;wherein the plurality of laboratory-implemented features comprises anenvelope modulation spectrum, a long-term average spectrum,spatio-temporal features, and dysphonia features; and processorcircuitry configured to (A) select a subset of the plurality oflaboratory-implemented features that is relevant for predicting theplurality of perceptual dimensions, and (B) utilize the subset of theplurality of laboratory-implemented features to generate and/or update apredictive software model that is configured to receive at least oneadditional patient speech sample and is configured to perform at leastone of the following items (a) or (b): (a) generate an objectiveevaluation of the plurality of perceptual dimensions utilizing the atleast one additional patient speech sample; or (b) evaluate at least oneof disease onset, disease progression, or disease treatment efficacy fora condition involving dysarthria as a symptom, utilizing the at leastone additional patient speech sample.

In certain embodiments, the processor circuitry is further configured toextract the plurality of laboratory-implemented features from theplurality of patient speech samples for inclusion in the data matrix.

In certain embodiments, the plurality of perceptual dimensions includeseach of nasality, prosody, articulatory precision, vocal quality, andseverity.

In certain embodiments, the selecting of the subset of the plurality oflaboratory-implemented features comprises use of lasso or

₁-regularized regression.

In certain embodiments, the processor circuitry is configured to selectthe subset of the plurality of laboratory-implemented features utilizingcross-validation and sparsity-based feature selection.

In certain embodiments, the system further comprises an audio inputconfigured to electronically receive the at least one additional patientspeech sample. In certain embodiments, the system further comprises adisplay generator configured to provide a displayable signal promptingat least one patient to read displayed text prior to, or concurrentlywith, electronic reception of the at least one additional patient speechsample.

In another aspect, the disclosure relates to a non-transitory computerreadable medium storing software instructions that, when executed by oneor more processors of a speech evaluation system, cause the speechevaluation system to: select a subset of a plurality oflaboratory-implemented features from a data matrix that includes (i) theplurality of laboratory-implemented features, wherein said plurality oflaboratory-implemented features is extracted from a plurality of patientspeech samples, and (ii) a plurality of subjective expert ratingscorresponding to the plurality of patient speech samples and involvingevaluations on a multi-point scale for a plurality of perceptualdimensions including two or more of nasality, prosody, articulatoryprecision, vocal quality, and severity; wherein the subset of theplurality of laboratory-implemented features is relevant for predictingthe plurality of perceptual dimensions; and wherein the plurality oflaboratory-implemented features comprises an envelope modulationspectrum, a long-term average spectrum, spatio-temporal features, anddysphonia features; and utilize the subset of the plurality oflaboratory-implemented features to generate and/or update a predictivemodel configured to receive at least one additional patient speechsample and to perform at least one of the following items (a) or (b):(a) generate an objective evaluation of the plurality of perceptualdimensions utilizing the at least one additional patient speech sample;or (b) evaluate at least one of disease onset, disease progression, ordisease treatment efficacy for a condition involving dysarthria as asymptom, utilizing the at least one additional patient speech sample.

In certain embodiments, the software instructions are further configuredto cause the speech evaluation system to: electronically receive theplurality of patient speech samples and the plurality of subjectiveexpert ratings; and extract the plurality of laboratory-implementedfeatures from the plurality of patient speech samples for inclusion inthe data matrix.

In certain embodiments, the software instructions are further configuredto cause the speech evaluation system to: electronically receive the atleast one additional patient speech sample; and generate an objectiveevaluation of the plurality of perceptual dimensions utilizing the atleast one additional patient speech sample.

In certain embodiments, the software instructions are further configuredto cause the speech evaluation system to: electronically receive the atleast one additional patient speech sample; and evaluate at least one ofdisease onset, disease progression, or disease treatment efficacy for acondition involving dysarthria as a symptom, utilizing the at least oneadditional patient speech sample.

In certain embodiments, the software instructions are further configuredto cause the speech evaluation system to prompt at least one patient toread displayed text prior to, or concurrently with, the electronicreceiving of the at least one additional patient speech sample.

In certain embodiments, the software instructions are further configuredto cause the speech evaluation system to provide user-perceptiblefeedback to at least one patient while the at least one patient readsthe displayed text, to alert the at least one patient to attainment ofone or more conditions indicative of a speech problem. In certainembodiments, the user-perceptible feedback comprises tactile feedback.

In certain embodiments, the plurality of perceptual dimensions includeseach of nasality, prosody, articulatory precision, vocal quality, andseverity.

In certain embodiments, the selecting of the subset of the plurality oflaboratory-implemented features comprises use of lasso or

₁-regularized regression.

In certain embodiments, the selecting of the subset of the plurality oflaboratory-implemented features comprises use of cross-validation andsparsity-based feature selection.

In yet another aspect, the disclosure relates to a system for evaluatingspeech, the system comprising: at least one memory configured to store aplurality of patient speech samples and a plurality of subjective expertratings corresponding to the plurality of patient speech samples,wherein each subjective expert rating of the plurality of subjectiveexpert ratings includes evaluation on a multi-point scale for aplurality of perceptual dimensions including nasality, prosody,articulatory precision, vocal quality, and severity; and processorcircuitry configured to (A) extract a plurality oflaboratory-implemented features from the plurality of patient speechsamples to generate a data matrix, wherein the plurality oflaboratory-implemented features comprises an envelope modulationspectrum, a long-term average spectrum, spatio-temporal features, anddysphonia features; (B) select a subset of the plurality oflaboratory-implemented features relevant for predicting the plurality ofperceptual dimensions; and (C) generate and/or update a predictivesoftware model that is configured to receive at least one additionalpatient speech sample and to perform at least one of (i) generating anobjective evaluation of the plurality of perceptual dimensions utilizingthe at least one additional patient speech sample or (ii) evaluating atleast one of disease onset, disease progression, or disease treatmentefficacy for a condition involving dysarthria as a symptom, utilizingthe at least one additional patient speech sample.

In certain embodiments, the system further comprises one or more signalinputs configured to (a) electronically receive the plurality of patientspeech samples, (b) electronically receive the plurality of subjectiveexpert ratings corresponding to the plurality of patient speech samples,and (c) electronically receive the at least one additional patientspeech sample.

In another aspect, the disclosure relates to a method for evaluatingspeech in a system involving processor circuitry, the method comprising:electronically receiving (i) a plurality of patient speech samples and(ii) a plurality of subjective expert ratings corresponding to theplurality of patient speech samples, wherein each subjective expertrating of the plurality of subjective expert ratings includes evaluationon a multi-point scale for a plurality of perceptual dimensionsincluding nasality, prosody, articulatory precision, vocal quality, andseverity; extracting a plurality of laboratory-implemented features fromthe plurality of patient speech samples to generate a data matrix,wherein the plurality of laboratory-implemented features comprises anenvelope modulation spectrum, a long-term average spectrum,spatio-temporal features, and dysphonia features; selecting a subset ofthe plurality of laboratory-implemented features relevant for predictingthe plurality of perceptual dimensions; and utilizing the subset of theplurality of laboratory-implemented features to generate and/or update apredictive software model that is configured to receive at least oneadditional patient speech sample and perform at least one of (a)generating an objective evaluation of the plurality of perceptualdimensions utilizing the at least one additional patient speech sampleor (b) evaluating at least one of disease onset, disease progression, ordisease treatment efficacy for a condition involving dysarthria as asymptom, utilizing the at least one additional patient speech sample.

In another aspect, the disclosure relates to a computer programcomprising instructions which, when executed by processor circuitryincluding at least one processor, cause the at least one processor tocarry out the method as disclosed herein.

In certain aspects, any of the preceding aspects or other featuresdisclosed here may be combined for additional advantage.

Those skilled in the art will appreciate the scope of the presentdisclosure and realize additional aspects thereof after reading thefollowing detailed description of the preferred embodiments inassociation with the accompanying drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level schematic diagram of operation of an algorithmfor extracting a set of laboratory-implemented features that captureirregularities in the speech of a specific patient, and that aresupplied to a decision engine (or other processor circuitry) that mayfurther receive subjective expert ratings for a plurality of perceptualdimensions of patient speech, according to one embodiment of the presentdisclosure.

FIG. 2 is a flowchart outlining steps in a method for evaluating speechincluding generation and use of a software model in whichlaboratory-implemented features that capture irregularities in speechare selected and used to predict five commonly assessed perceptualdimensions (nasality, prosody, articulatory precision, vocal quality,and severity) for objective evaluation of the perceptual dimensionsand/or evaluation of at least one of disease onset, disease progression,or disease treatment efficacy for a condition involving dysarthria as asymptom, according to one embodiment of the present disclosure.

FIG. 3 is a schematic showing a speech sample (sound) acquisitionmodule, a tactile feedback module, and a signal processing module(“signal processor”, e.g., for objective feature extraction) that may beused with a patient as components of a sample acquisition subsystemuseful with one or more embodiments disclosed herein.

FIG. 4 illustrates components of an exemplary speech evaluation systemcomprising a laptop computer, a headset, and a signal processing module,according to one embodiment of the present disclosure.

FIG. 5 is flow chart depicting a method of monitoring and detectingerrors in audio signals containing speech and providing auser-perceptible alert signal, as may be used in obtaining speechsamples for use with one or more embodiments disclosed herein.

FIG. 6 is a schematic showing interconnections between components of anexemplary speech evaluation system, including speech sample acquisition,processing circuitry, and network elements that may be used in one ormore embodiments disclosed herein.

FIG. 7A illustrates a first graphical user interface screen foreliciting a patient to provide a speech sample for acquisition by aspeech evaluation system according to one embodiment of the presentdisclosure.

FIG. 7B illustrates a second graphical user interface screen forpermitting a speech-language pathologist (or other clinician) toadminister or review a speech sample for use with the speech evaluationsystem of FIG. 7A, according to one embodiment of the presentdisclosure.

FIG. 8 illustrates superimposed third and fourth graphical userinterface screens for a speech evaluation system, according to oneembodiment of the present disclosure.

FIG. 9 is a perspective view illustration of a behind-the-neck headsetdevice useable with a speech evaluation system, according to oneembodiment of the present disclosure.

FIGS. 10A and 10B provide side elevation views of two halves of abehind-the-ear device incorporating electronic circuitry useable forspeech sample acquisition and/or processing and useable with a speechevaluation system, according to one embodiment of the presentdisclosure.

FIG. 11 is a schematic diagram of a speech evaluation system providingan interface for a speech-language pathologist via a client device,wherein a speech sample may be gathered remotely from a patient via acommunication device, according to one embodiment of the presentdisclosure.

FIGS. 12A-12C are schematic diagrams of electronic circuitry accordingto one implementation of a speech evaluation system, according to oneembodiment of the present disclosure.

FIG. 13 is a printed circuit board (PCB) layout diagram for a signalprocessing module for use with a speech evaluation system according toone embodiment of the present disclosure.

DETAILED DESCRIPTION

The embodiments set forth herein represent the necessary information toenable those skilled in the art to practice the embodiments andillustrate the best mode of practicing the embodiments. Upon reading thedescription in light of the accompanying drawing figures, those skilledin the art will understand the concepts of the disclosure and willrecognize applications of these concepts not particularly addressedherein. It should be understood that these concepts and applicationsfall within the scope of the disclosure and the accompanying claims.

In certain aspects, the present disclosure relates to a method forevaluating speech, a system for evaluating speech, a non-transitorycomputer readable medium storing software instructions, and a computerprogram including instructions for causing a processor to carry out amethod.

In certain embodiments, a data matrix may be generated, said data matrixincorporating processed speech samples and speech-language pathologistratings corresponding to the speech samples. Processing of the speechsamples includes extraction of a plurality of laboratory-implementedfeatures (e.g., an envelope modulation spectrum, a long-term averagespectrum, spatio-temporal features, and dysphonia features). Thespeech-language pathologist ratings include subjective multi-pointratings of commonly assessed perceptual dimensions (e.g., two, three,four, or all five of nasality, prosody, articulatory precision, vocalquality, and severity). A subset of the plurality oflaboratory-implemented features that is relevant for predicting aplurality of perceptual dimensions, and that simplifies computation byreducing multi-collinearity, is selected. The subset includes a uniqueset of laboratory-implemented features per dimension, and data thereinmay be centered and reduced to a manageable number of features (e.g., nogreater than about 50, about 40, about 30, or about 25 features perperceptual dimension). The resulting feature set may be employed as aninput to a predictive software model (e.g., an objective evaluationlinear model) that predicts objective ratings from the down-selected andcentered feature set representative of speech acoustics. The predictivesoftware model captures the relationship between speech acoustics andsubjective ratings. Cross-validation (or more preferably a combinationof cross-validation and sparsity based-feature selection) may be used togenerate and/or update (e.g., calibrate) a predictive software modelthat is configured to receive at least one additional patient speechsample and perform at least one of (a) generating an objectiveevaluation of the plurality of perceptual dimensions utilizing the atleast one additional patient speech sample or (b) evaluating at leastone of disease onset, disease progression, or disease treatment efficacyfor a condition involving dysarthria as a symptom, utilizing the atleast one additional patient speech sample. In certain embodiments, theobjective evaluation of the plurality of perceptual dimensions includesa multi-point evaluation spanning all five dimensions outlined above.

The subject matter described herein may be implemented in hardware,software, firmware, or any combination thereof. As such, the terms“function” or “module” may be used herein to refer to hardware,software, and/or firmware for implementing the feature being described.

In one exemplary implementation, the subject matter described herein maybe implemented using a computer readable medium having stored thereonexecutable instructions that, when executed by the processor of acomputer, direct the computer to perform steps. Exemplary computerreadable media suitable for implementing the subject matter describedherein include disk memory devices (e.g., a compact disc (CD) or adigital video disc (DVD)), chip memory devices (e.g., a USB drive ormemory card), programmable logic devices, application specificintegrated circuits, network storage devices, and other non-transitorystorage media. In one implementation, the computer readable medium mayinclude a memory accessible by a processor of a computer or other likedevice. The memory may include instructions executable by the processorfor implementing any of the methods described herein. In addition, acomputer readable medium that implements the subject matter describedherein may be located on a single device or computing platform, or maybe distributed across multiple physical devices and/or computingplatforms. An exemplary processor (also referred to as a processorcircuit or processor circuitry) may comprise microprocessor(s), CentralProcessing Unit(s) (CPU(s)), Application Specific Integrated Circuit(s)(ASIC(s)), Field Programmable Gate Array(s) (FPGA(s)), or the like.

An initial step in building a predictive software model or decisionengine is formation of a data matrix. For all speech samples in adatabase, a series of laboratory-implemented features are extracted.These laboratory-implemented features include two or more (or morepreferably all of) the envelope modulation spectrum, the long-termaverage spectrum, spatio-temporal features, and dysphonia features. Suchfeatures are described hereinafter.

The envelope modulation spectrum (EMS) is a representation ofslow-amplitude modulations in a signal and the distribution of energy inamplitude fluctuations across designated frequencies, collapsed overtime. EMS has been shown to be a useful indicator of atypical rhythmpatterns in pathological speech.

Each speech segment in a preexisting pathological speech database, x(t),is filtered into 7 octave bands with center frequencies of 125, 250,500, 1000, 2000, 4000, and 8000 Hz. h_(i)(t) denotes the filterassociated with the i^(th) octave. The filtered signal, x_(i)(t), isthen denoted by:

x _(i)(t)=*x(t)

The envelope in the i^(th) octave, denoted by e_(i)(t), is extracted by:

e _(i)(t)=h _(LPF)(t)*H(x(t))

where H(.) is the Hilbert transform and h_(LPF)(t) is the impulseresponse of a 20 Hz low-pass filter.

Once the amplitude envelope of the signal is obtained, the low-frequencyvariation in the amplitude levels of the signal can be examined. Fourieranalysis quantifies the temporal regularities of the signal. Six EMSmetrics are then computed from the resulting envelope spectrum for eachof the 7 octave bands, x_(i)(t), and the full signal, x(t): 1) Peakfrequency, 2) Peak amplitude, 3) Energy in the spectrum from 3-6 Hz, 4)Energy in the spectrum from 0-4 Hz, 5) Energy in the spectrum from 4-10Hz, and 6) Energy ratio between 0-4 Hz band and 4-10 Hz band.

The long-term average spectrum (LTAS) captures atypical average spectralinformation in the signal. Nasality, breathiness, and atypical loudnessvariation, which are common causes of intelligibility deficits indysarthric speech, present as atypical distributions of energy acrossthe spectrum; LTAS measures these cues in each octave. For each of the 7octave bands, x_(i)(t), and the full signal, x(t), the following areextracted: 1) average normalized root mean square (RMS) energy, 2) RMSenergy standard deviation, 3) RMS energy range, and 4) pairwisevariability of RMS energy between ensuing 20 ms frames.

The spatio-temporal features capture the evolution of vocal tract shapeand dynamics in different time scales via auto- and cross-correlationanalysis of formant tracks and mel-frequency cepstral coefficients(MFCC).

The dysphonia features capture atypical vocal quality through theanalysis of pitch changes and pitch amplitude changes over time.

The data matrix generated by processing the speech samples andextracting the laboratory-implemented features results in highdimensional data. Regression in high dimensional space is notoriouslydifficult: dimensionality requires exponential growth in the number ofexemplars as the intrinsic dimension of the data increases. Thus, aprocessor-implemented routine is constructed and implemented to selectonly a relevant subset of these features, through a combination ofcross-validation and sparsity-based feature selection (e.g., involvinglasso or

₁-regularized regression). Restated, subsets of acoustic metrics thatmap to perceptual ratings are identified. The selection criterion aimsto (1) identify a subset of laboratory-implemented features that arerelevant for predicting each of the five perceptual dimensions(nasality, prosody, articulatory precision, vocal quality, and severity)and (2) reduce the multi-collinearity problem, thereby enablingpractical computation. This subset selection results in a unique set offeatures per perceptual dimension. Following this down-selection,principal components analysis may be used to center the data and furtherreduce the feature set to a manageable number (e.g., no greater thanabout 50, about 40, about 30, or about 25) for each dimension. This newcentered feature set may advantageously be used as an input to thepredictive software model, to permit objective evaluation of theplurality of perceptual dimensions (nasality, prosody, articulatoryprecision, vocal quality, and severity) from an additional patientspeech sample. Automated acoustic measures disclosed herein arespecifically designed to address challenges of dysarthric speechanalysis.

For each perceptual dimension, the predictive software model (e.g., anobjective evaluation linear model) predicts an objective rating(optionally expressed on a multi-point such as a 7-point scale) from thedown-selected and centered speech acoustics. In certain embodiments,cross-validation is used to train the predictive software model.Cross-validation involves partitioning the data matrix intocomplementary subsets, learning the parameters of the decision engine onone subset (training speakers), and validating on the remaining subset(testing speakers). The error on the (held out) test data set is used toassess the predictive power of the predictive software model. Aframework for generating a predictive software model utilizingcross-validation and sparsity-based feature selection (e.g., lasso or

₁-regularized regression) follows.

In general, a sparse statistical model is one in which only a relativelysmall number of parameters (or predictors) play an important role.

A leading example of a method that employs sparsity is linearregression, in which N observations of an outcome variable y_(i) and passociated predictor variables (or features) x_(i)−(x_(i1), . . .x_(ip))^(T) are observed. The goal is to predict an outcome from thepredictors—both for actual prediction of future data and also todiscover which predictors play an important role. A linear regressionmodel assumes that:

${y_{i} = {\beta_{0} + {\sum\limits_{j = 1}^{p}{x_{ij}\beta_{j}}} + e_{i}}},$

where β₀ and β=(β₁, β₂, . . . β_(p)) are unknown parameters and e_(i) isan error term. The method of least-squares provides estimates of theparameters by minimization of the least-squares objective function:

$\underset{\beta_{0},\beta}{minimize}\mspace{11mu} {\sum\limits_{i = 1}^{N}\left( {y_{i} - \beta_{0} - {\sum\limits_{j = 1}^{p}{x_{ij}\beta_{j}}}} \right)^{2}}$

One limitation with the least-squares method is that interpretation ofthe final model is challenging if p is large. If p>N, then theleast-squares estimates are not unique. In such a situation, an infiniteset of solutions will make the objective function equal to zero, andthese solutions tend to overfit the data as well.

In view of the limitations of the least-squares method, there is a needto constrain, or regularize, the estimation process. Such need isaddressed by “lasso” or “

₁-regularized” regression, in which parameters are estimated by solvingthe problem:

${\underset{\beta_{0},\beta}{minimize}\mspace{11mu} {\sum\limits_{i = 1}^{N}{\left( {y_{i} - \beta_{0} - {\sum\limits_{j = 1}^{p}{x_{ij}\beta_{j}}}} \right)^{2}\mspace{11mu} {subject}\mspace{14mu} {to}\mspace{14mu} {\beta }_{1}}}} \leq t$${{{where}\mspace{14mu} {\beta }_{1}} = {\sum_{i = 1}^{p}{{\beta_{j}}\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} _{1}\mspace{14mu} {norm}\mspace{14mu} {of}\mspace{14mu} \beta}}},$

where t is a user-specified parameter. The parameter t can be considereda budget on the total

₁ norm of the parameter vector, and the lasso finds the best fit withinthis budget. If the budget tis small enough, the lasso yields sparsesolution vectors, having only some coordinates that are nonzero. Thebound tin the lasso criterion is a kind of budget, in that it limits thesum of the absolute values of the parameter estimates, and controls thecomplexity of the model. In particular, larger values of t free up moreparameters and allow the model to adapt more closely to the trainingdata. Conversely, smaller values of t restrict the parameters more,leading to sparser, more interpretable models that fit the data lessclosely. The

₁-norm represents the smallest value that yields a convex problem.Convexity simplifies the computation, and allows for scalable algorithmsthat can handle problems with a multitude of parameters.

The advantages of sparsity are therefore interpretation of the fittedmodel and computational convenience. But in recent years, a thirdadvantage has emerged from mathematical analysis of this area, with suchadvantage being termed the “bet on sparsity” principle, namely: Use aprocedure that does well in sparse problems, since no procedure doeswell in dense problems.

The lasso estimator for linear regression is a method that combines theleast-squares loss with an

₁-constraint (or bound) on the sum of the absolute values of thecoefficients. Relative to the least-squares solution, this constrainthas the effect of shrinking the coefficients, and even setting some tozero. In this way, it provides an automatic method for performing modelselection in linear regression. Moreover, unlike some other criteria formodel selection, the resulting optimization problem is convex, and canbe solved efficiently for large problems.

Given a collection of N predictor-response pairs {(x_(i),y_(i))}_(i=1)^(N), the lasso finds the solution (

, {circumflex over (β)}) to the optimization problem:

$\underset{\beta_{0},\beta}{minimize}\left\{ {\frac{1}{2N}{\sum\limits_{i = 1}^{N}\left( {y_{i} - \beta_{0} - {\sum\limits_{j = 1}^{p}{x_{ij}\beta_{j}}}} \right)^{2}}} \right\}$${{subject}\mspace{14mu} {to}\mspace{14mu} {\sum\limits_{j = 1}^{p}{\beta_{j}}}} \leq {t.}$

The preceding (“subject to . . . ”) constraint can be written morecompactly as the

₁-norm constraint ∥β∥₁≤t. Furthermore, the lasso optimization problemoutlined above is often represented using matrix-vector notation. Ify=(y₁, . . . , y_(N)) denotes the N-vector of responses and X is an N×pmatrix with x_(i) ∈ R^(p) in its i^(th) row, then the lasso optimizationproblem can be re-expressed as:

$\underset{\beta_{0},\beta}{minimize}\left\{ {\frac{1}{2N}{{y - {\beta_{0}1} - {X\; \beta}}}_{2}^{2}} \right\}$subject  to  β₁ ≤ t,

where 1 is the vector of N ones, and ∥·∥₂ denotes the usual Euclideannorm on vectors.

The predictors X may be standardized so that each column is centeredaccording to:

$\left( {{\frac{1}{N}{\sum_{i = 1}^{N}x_{ij}}} = 0} \right)$

and has unit variance:

$\left( {{\frac{1}{N}{\sum_{i = 1}^{N}x_{ij}^{2}}} = 1} \right).$

Without standardization, the lasso solutions would depend on the units(e.g., pounds vs. kilograms, or meters vs. feet) used to measure thepredictors, but standardization would not be necessary if all featureswere measured in the same units. For convenience, the outcome valuesy_(i) may be centered (such that the intercept term B_(o) can be omittedin the lasso optimization), with such centering meaning that:

${\frac{1}{N}{\sum_{i = 1}^{N}y_{i}}} = 0.$

It is often convenient to rewrite the lasso problem in the so-calledLagrangian form:

${\underset{\beta \in {{\mathbb{R}}\; p}}{minimize}\left\{ {{\frac{1}{2N}{{y - {X\; \beta}}}_{2}^{2}} + {\lambda {\; \beta }_{1}}} \right\}},$

for some λ≥0. By Lagrangian duality, there is a one-to-onecorrespondence between the constrained problem (i.e., minimization ofβ₀, β) and the Lagrangian form. That is, for each value of t solving the

₁-norm constraint ∥β∥₁≤t, there is a corresponding value of λ thatyields the same solution from the Lagrangian form.

In order to estimate this best value for t, artificial training and testsets can be created by splitting up the given dataset at random, andestimating performance on the test data, using cross-validation. Onegroup may be fixed as the test set, and the remaining groups may bedesigned as the training set. The lasso may be applied to the trainingdata for a range of different values, and each fitted model may be usedto predict the responses in the test set, recording the mean-squaredprediction errors for each value of t. This process is repeated a totalnumber of times equal to the number of groups of data. In this way, anumber of different estimates of the prediction error are obtained overa range of values of t.

The lasso problem is a quadratic problem with a convex constraint. Manysophisticated quadratic program methods exist for solving the lasso. Onesimple and effective computational algorithm that may be employedutilizes the criterion in Lagrangian form, namely:

$\underset{\beta \in {{\mathbb{R}}\; p}}{minimize}{\left\{ {{\frac{1}{2N}{\sum\limits_{i = 1}^{N}\left( {y_{i} - {\sum\limits_{j = 1}^{p}{x_{ij}\beta_{j}}}} \right)^{2}}} + {\lambda {\sum\limits_{j = 1}^{p}{\beta_{j}}}}} \right\}.}$

It may be assumed that y_(i) and the features x_(ij) may be standardizedso that:

${{\frac{1}{N}{\sum_{i}y_{i}}} = 0},{{\frac{1}{N}{\sum_{i}x_{ij}}} = 0},{{{and}\mspace{14mu} \frac{1}{N}{\sum_{i}x_{ij}^{2}}} = 1}$

and the intercept term β₀ can be omitted. The Lagrangian form isespecially useful for numerical computation of the solution by a simpleprocedure known as coordinate descent. A simple coordinate-wise schemefor solving the lasso problem involves repeatedly cycling through thepredictors in a fixed (but arbitrary) order (e.g., j=1, 2, . . . p),wherein at the j^(th) step, the coefficient β_(j) is updated byminimizing the objective function in this coordinate while holding fixedall other coefficients {

, k≠j} at their current values.

If the Lagrangian form objective is rewritten as:

${{\frac{1}{2N}{\sum\limits_{i = 1}^{N}\left( {y_{i} - {\sum\limits_{k \neq j}{x_{ik}\beta_{k}}} - {x_{ij}\beta_{j}}} \right)^{2}}} + {\lambda {\sum\limits_{k \neq j}{\beta_{k}}}} + {\lambda {\beta_{j}}}},$

then the solution for each β_(j) can be expressed in terms of the

partial residual r _(i) ^((j)) =y _(i)−Σ_(k≠j) x _(ik){circumflex over(β)}_(k),

which removes, from the outcome, the current fit from all but the j^(th)predictor. In terms of this partial residual, the j_(th) coefficient isupdated as:

${\hat{\beta}}_{j} = {{_{\lambda}\left( {\frac{1}{N}{\langle{x_{j},r^{(j)}}\rangle}} \right)}.}$

(In the preceding equation, S_(λ) represents a soft-thresholdingoperation S_(λ)(x) that translates its argument x toward zero by theamount A and sets it to zero if ∥x∥≤λ.)Equivalently, the update can be written as:

$\left. {\hat{\beta}}_{j}\leftarrow{_{\lambda}\left( {{\hat{\beta}}_{j} + {\frac{1}{N}{\langle{x_{j},r}\rangle}}} \right)} \right.,$

where the full residuals are:

r _(i) =y _(i)−Σ_(j=1) ^(p) x _(ij){circumflex over (β)}_(j).

The numerical computation algorithm operates by applying thissoft-thresholding update repeatedly in a cyclical manner, updating thecoordinates of {circumflex over (β)} (and therefore the residualvectors) along the way. Such algorithm corresponds to the method ofcyclical coordinate descent, which minimizes the convex objective alongeach coordinate at a time. Under relatively mild conditions, suchcoordinate-wise minimization schemes applied to a convex functionconverge to a global optimum.

In other embodiments, a method of pathwise coordinate descent may beused to compute a lasso solution not only for a single fixed value of λ,but rather an entire path of solutions over a range of possible λvalues. Such a method may begin with a value of λ just large enough thatthe only optimal solution is the all-zeroes vector, and then repeatedlydecreasing λ by a small amount and running coordinate descent untilconvergence.

In certain embodiments, one or more routines or algorithms of thepredictive software model may be implemented in R programming language,which is an open source programming language and software environment. Ris a GNU package that is supported by the R Foundation for StatisticalComputing (Vienna, Austria). If desired, other programming languages orsoftware environments may be employed.

FIG. 1 is a high-level schematic diagram of operation of an algorithm 10for automatically extracting laboratory-implemented features 20 thatcapture irregularities in patient speech samples, and that are suppliedto a decision engine 26 (or other processor circuitry) that may furtherreceive corresponding subjective expert (e.g., speech-languagepathologist) ratings 24 for a plurality of perceptual dimensions ofpatient speech, according to one embodiment of the present disclosure.EMS features 12, LTAS features 14, spatio-temporal features 16, anddysphonia features 18 are identified from a database 11 of dysarthricspeech samples, with outputs supplied to the decision engine 26.Subjective expert ratings 24 corresponding to the extracted set oflaboratory-implemented features 20 are also supplied from an SLP ratingsdatabase 22.

As noted previously, existing objective measures in speech and languageclinics focus on measuring aspects of speech signals that are notinterpretable in clinical settings. Examples of such objective measuresinclude instruments that measure pitch, formants, energy, and othersimilar metrics.

In contrast to these existing objective measures in speech and languageclinics, embodiments according to the present disclosure are useful forbridging the subjective-objective divide by blending the face validityof perceptual assessment with the reliability of objective measures.Advances in signal processing and machine-learning in conjunction withthe present disclosure are leveraged to model expert perceptualjudgments, and to facilitate predictive software modeling of perceptualratings of speech. Comparisons of outcomes between laboratory data andthose collected in clinical settings inform the theories that supportthe model with real-world data. Technical capabilities will advance withthe refinement of the speech algorithms to optimize their performance.Technology that affords stable objective measures of speech that map toexpert perceptual ratings is anticipated to have high clinical impact.In particular, systems and methods disclosed herein may offer a platformto sensitively assess treatment efficacy, disease onset, and diseaseprogression, etc. with unbiased perception-calibrated metrics.

While acoustic analysis of disordered speech is commonplace in research,technology has yet to be developed that adds clinical value. Theapproach disclosed herein is novel in several ways.

In certain embodiments, signal processing capabilities and machinelearning algorithms may be leveraged to model (weighted) perceptions ofexperts (e.g., speech-language pathologists) in the generation and useof a predictive software model. Thus, the output of the predictivesoftware model is immediately clinically transparent, and does notrequire any norms or references for comparison.

In certain embodiments, predictive software models disclosed herein are“learners,” meaning that the algorithms become more refined with eachiteration.

In certain embodiments, systems and methods disclosed herein may beintegrated in a telehealth platform. This would be transformative byexpanding videoconference capabilities of current remote methods toprovide analytical capabilities.

FIG. 2 is a flowchart outlining steps in a method 28 for evaluatingspeech, including generation and use of a software model in whichlaboratory-implemented features that capture irregularities in thespeech are selected and used to predict five commonly assessedperceptual dimensions (nasality, prosody, articulatory precision, vocalquality, and severity) for objective evaluation of the perceptualdimensions and/or evaluation of at least one of disease onset, diseaseprogression, or disease treatment efficacy for a condition involvingdysarthria as a symptom, according to one embodiment of the presentdisclosure. The method 28 includes receiving speech samples according tostep 30 (e.g., speech samples for multiple patients exhibitingdysarthria), extracting laboratory-implemented features according tostep 32 (e.g., an envelope modulation spectrum, a long-term averagespectrum, spatio-temporal features, and dysphonia features) from thereceived speech samples, and supplying the laboratory-implementedfeatures to generate a data matrix according to step 36. A parallel stepincludes receiving subjective multi-point (e.g., 7 point) ratingsaccording to step 34 generated by experts (e.g., speech-languagepathologists) for the same speech samples as used in steps 30, 32, andsupplying the ratings to the data matrix. Thereafter, the methodincludes selecting a subset of a plurality of laboratory-implementedfeatures according to step 38 from the data matrix, wherein the subsetof the plurality of laboratory-implemented features is relevant forpredicting the plurality of perceptual dimensions, and preferably alsoreduces multi-collinearity. In certain embodiments, the subset offeatures may be down-selected and centered. According to decision block40, if a predictive software model has not yet been created, then apredictive software model (e.g., an objective evaluation linear model)is created at step 42; otherwise, a pre-existing predictive softwaremodel is updated according to step 44. In certain embodiments, selectionof the subset of the plurality of laboratory-implemented featuresrelevant to prediction of the plurality of perceptual dimensions mayinclude the use of lasso or

₁-regularized regression, or more specifically the use of a combinationof cross-validation and sparsity-based feature selection. Followinggenerating or updating of the predictive software model, an additionalpatient speech sample may be obtained for processing with the predictivesoftware model. According to step 46, a patient may be prompted (e.g.,by a visual display device) to read text, optionally in conjunction withthe provision to the patient of user-perceptible (e.g., tactile,visible, auditory, or the like) feedback while the at least one patientreads the displayed text, to alert the patient to attainment of one ormore conditions indicative of a speech problem. Upon generation of theadditional speech sample, such sample may be received (e.g.,electronically received) by a speech evaluation system incorporating thepredictive software model according to step 48. Operation of thepredictive software model on the additional speech sample may result inone or more of (a) generating an objective evaluation of the pluralityof perceptual dimensions utilizing the at least one additional patientspeech sample, according to step 54; or (b) evaluating disease and/ortreatment state (e.g., at least one of disease onset, diseaseprogression, or disease treatment efficacy) for a condition involvingdysarthria as a symptom, according to step 50. With respect toperformance of the steps of either or both of steps 50, 54, a clinicianmay be notified of the result of the evaluation and an electronicpatient record may be stored or updated according to steps 52, 66.Moreover, following performance of the step of step 54, results of theobjective evaluation of the plurality of perceptual dimensions utilizingthe at least one additional patient speech sample may be supplied to thepredictive software model to enable the model to be updated, byreturning to step 44.

FIG. 3 is a schematic showing a speech sample (e.g., sound) acquisitionmodule 64, a tactile feedback module 68, and a signal processing module66 (“signal processor”, e.g., for objective feature extraction) that maybe used with a patient 62 as components of a sample acquisitionsubsystem 60 useful with one or more embodiments disclosed herein. Thesound acquisition module 64 may embody a microphone (or similartransducer) or a signal receiver for receiving speech (or a signalindicative of speech) from the patient 62 and for producing (orreceiving) a speech sample as an analog electrical signal, which may bedigitized thereafter for subsequent processing. The sound acquisitionmodule 64 is operatively coupled with the signal processing module 66,which may be used to (a) determine whether a speech error is present,and/or (b) process the received speech sample according to any suitableprocessing steps disclosed herein. The signal processing module 66 mayfurther make decisions as to what types of alert signal(s) should bepresented to the user, and may further log details corresponding toalerts and/or audio sample status. The tactile feedback module 68 mayfurther be arranged to receive one or more speech error signals from thesignal processing module 66, and provide a user-perceptible alert signalto the patient 62.

FIG. 4 illustrates components of an exemplary speech evaluation system70 comprising a laptop computer 71, a headset 74, and a signalprocessing module 79, according to one embodiment of the presentdisclosure. The headset 74 includes ear-contacting portions 76 and aclose-talk microphone 78. The signal processing module 79 includes aSTEVAL-CCA023V1 demonstration board (STMicroelectronics, Geneva,Switzerland). To provide tactile feedback, the headset 74 may include alinear resonant actuator and a DRV2605EVM-CT driver board (TexasInstruments, Dallas, Tex.). In certain embodiments, tactile feedback maybe provided to a patient while supplying a speech sample via theclose-talk microphone 78 to components of the speech evaluation system70. While a separate signal processing module 79 is shown in FIG. 4 asintermediately arranged between the headset 74 and the laptop computer71, in certain embodiments, the headset 74 may be coupled directly tothe laptop computer 71 without requiring a dedicated signal processingmodule 79. In certain embodiments, some or all of the method stepsdescribed in connection with FIG. 2 may be performed using the speechevaluation system 70. The laptop computer 71 includes a non-transientcomputer readable medium such as a hard disk drive. The non-transientcomputer readable medium may include program instructions for causingthe laptop computer 71 to perform method steps such as described inconnection with FIG. 2 or otherwise disclosed herein. A display of thelaptop computer 71 may be used to display text and instructions toprompt a patient to supply one or more speech samples to the headset 74for capture and use by the speech evaluation system 70.

FIG. 5 is a flow chart depicting a method 80 for eliciting andmonitoring speech provided by a patient and providing a user-perceptiblealert signal, which may be used for therapeutic treatment. The method 80comprises an alert delay algorithm provided by a processor in a timedomain. Although the term “processor” is used in a singular sense, it isto be appreciated that in certain embodiments, multiple processors maybe employed, and optionally may be associated with different electronic(e.g., computing) devices. In step 82, the processor is configured todefine, or receive definitions for, variables i, j, k, m. In particular,the processor initially sets a counter j to a maximum value (e.g.,j=j_max). In step 84, the processor is configured to receive audiosamples. The processor is configured to monitor audio samples associatedwith the speech. In particular, in steps 86, the processor detectswhether the audio samples contain speech signals by calculating energylevel and cross-zero-rate, and in steps 88, the processor determineswhether a speech error is present through a speech error detectionalgorithm module (e.g., the speech level is below the volume levelthreshold, or another speech error condition is present). In certainembodiments, the processor may monitor multiple (e.g., 10, 20, 30, 40,50 or more) audio samples per second and provide a multi-second (e.g.,2, 3, 4, 5, 6, 7, 8, 9, or 10 second) delay for the time interval beforeproviding an alert signal. In the embodiment depicted in FIG. 5, themaximum value of the counter j is set to 250. In steps 88, if a speecherror is detected, then the counter decrements by 1 for each consecutiveaudio sample in which the speech level is below the volume levelthreshold. In steps 90, when the counter reaches zero or a negativevalue, the processor provides the alert signal. In other words, when theprocessor detects speech signals in steps 86 (e.g., i=1), those speechsignals are processed through a first-in-first-out buffer (e.g., m=1),and when a speech error has been consistently detected (j<0; k=1) thenthe processor initiates an alert signal. The processor may terminate thealert signal if one or more of the foregoing conditions cease to betrue.

FIG. 6 is a schematic showing interconnections between components of aspeech evaluation system 100 including a speech therapeutic device 72,processing circuitry 110 (with associated memory 112), a network 104,and a server 106. The speech therapeutic device 72 includes audio inputcircuitry 108 and stimulus circuitry 114. The audio input circuitry 108and stimulus circuitry 114 may be coupled with the processing circuitry110 via wired connections, wireless connections, or a combinationthereof. The speech therapeutic device 72 may further comprise abehind-the-ear device, an ear-mold device, a headset, a headband, asmartphone, or a combination thereof. The speech therapeutic device 72may be configured to receive speech 116 from a patient 62 and provide astimulus 120 to the patient 62 based on processing of the speech 116.

The audio input circuitry 108 may include at least one microphone. Incertain embodiments, the audio input circuitry 108 may include a boneconduction microphone, a near field air conduction microphone array, ora combination thereof. The audio input circuitry 108 may be configuredto provide an input signal 122 that is indicative of the speech 116provided by the patient 62 to the processing circuitry 110. The inputsignal 122 may be formatted as a digital signal, an analog signal, or acombination thereof. In certain embodiments, the audio input circuitry108 may provide the input signal 122 to the processing circuitry 110over a personal area network (PAN). The PAN may comprise UniversalSerial Bus (USB), IEEE 1394 (FireWire) Infrared Data Association (IrDA),Bluetooth, ultra-wideband (UWB), Wi-Fi Direct, or a combination thereof.The audio input circuitry 108 may further comprise at least oneanalog-to-digital converter (ADC) to provide the input signal 122 indigital format.

The processing circuitry 110 may include a communication interface (notshown) coupled with the network 104 and a processor (e.g., anelectrically operated processor (not shown) configured to execute apre-defined and/or a user-defined machine readable instruction set, suchas may be embodied in computer software) configured to receive the inputsignal 122. The communication interface may include circuitry forcoupling to the PAN, a local area network (LAN), a wide area network(WAN), or a combination thereof. The processing circuitry 110 isconfigured to communicate with the server 106 via the network 104. Incertain embodiments, the processing circuitry 110 may include an ADC toconvert the input signal 122 to digital form. In other embodiments, theprocessing circuitry 110 may be configured to receive the input signal122 from the PAN via the communication interface. The processingcircuitry 110 may further comprise level detect circuitry, adaptivefilter circuitry, voice recognition circuitry, or a combination thereof.The processing circuitry 110 may be further configured to process theinput signal 122 and to provide an alert signal 124 to the stimuluscircuitry 114.

The processor may be further configured to generate a record indicativeof the alert signal 124. The record may comprise a rule identifier andan audio segment indicative of the speech 116 provided by the patient62. In certain embodiments, the audio segment may have a total timeduration of at least one second before the alert signal 124 and at leastone second after the alert signal 124. Other time intervals may be used.For example, in other embodiments, the audio segment may have a totaltime duration of at least three seconds, at least five seconds, or atleast ten seconds before the alert signal 124 and at least threeseconds, at least five seconds, or at least ten seconds after the alertsignal 124. In other embodiments, at least one reconfigurable rule maycomprise a pre-alert time duration and a post-alert time duration,wherein the audio segment may have a total time duration of at least thepre-alert time duration before the alert signal 124 and at least thepost-alert time duration after the alert signal 124. In certainembodiments, the foregoing audio segments may be used as patient speechsamples according to speech evaluation systems and methods disclosedherein. By identifying conditions indicative of speech errors in speechsamples, samples exhibiting indications of dysarthria may be identified(e.g., flagged) and preferentially stored, aggregated, and/or used by aspeech evaluation system.

A record corresponding to a speech sample may optionally include alocation identifier, a time stamp, or a combination thereof indicativeof the alert signal 124. The location identifier may comprise a GlobalPositioning System (GPS) coordinate, a street address, a contact name, apoint of interest, or a combination thereof. In certain embodiments, acontact name may be derived from the GPS coordinate and a contact listassociated with the patient 62. The point of interest may be derivedfrom the GPS coordinate and a database including a plurality of pointsof interest. In certain embodiments, the location identifier may be afiltered location for maintaining the privacy of the patient 62. Forexample, the filtered location may be “user's home”, “contact's home”,“vehicle in transit”, “restaurant”, or “user's work”. In certainembodiments, the at least one reconfigurable rule may comprise alocation type, wherein the location identifier is formatted according tothe location type.

The processing circuitry 110 is configured to communicate with thememory 112 for storage and retrieval of information, such as subroutinesand data utilized in predictive software models—including (but notlimited) to patient speech samples, subjective expert ratingscorresponding to patient speech samples, and subsets oflaboratory-implemented features. The memory 112 may be a non-volatilememory, a volatile memory, or a combination thereof. The memory 112 maybe wired to the processing circuitry 110 using an address/data bus. Incertain embodiments, the memory 112 may be a portable memory coupledwith the processor via the PAN.

The processing circuitry 110 may be further configured to transmit oneor more records via the network 104 to the server 106. In certainembodiments, the processor may be further configured to append a deviceidentifier, a user identifier, or a combination thereof to the record. Adevice identifier may be unique to the speech therapeutic device 72, anda user identifier may be unique to the patient 62. The device identifierand the user identifier may be useful to a speech-language pathologistor other speech therapeutic professional, wherein the patient 62 may bea patient of the pathologist or other professional.

The stimulus circuitry 114 is configured to receive the alert signal 124and may comprise a vibrating element, a speaker, a visual indicator, ora combination thereof. In certain embodiments, the alert signal 124 mayencompass a plurality of alert signals including a vibrating elementsignal, a speaker signal, a visual indicator signal, or a combinationthereof. In certain embodiments, a speaker signal may include an audiosignal, wherein the processing circuitry 110 may provide the audiosignal as voice instructions for the patient 62.

The network 104 may comprise a PAN, a LA), a WAN, or a combinationthereof. The PAN may comprise Universal Serial Bus (USB), IEEE 1394(FireWire) Infrared Data Association (IrDA), Bluetooth, ultra-wideband(UWB), Wi-Fi Direct, or a combination thereof. The LAN may includeEthernet, 802.11 WLAN, or a combination thereof. The network 104 mayalso include the Internet. The server 106 may comprise a personalcomputer (PC), a local server connected to the LAN, a remote serverconnected to the WAN, or a combination thereof. In certain embodiments,the server 106 may be a software-based virtualized server running on aplurality of servers.

As used herein, the term “audio sample” may refer to a single discretenumber associated with an amplitude at a given time. Certain embodimentsmay utilize a typical audio sampling rate of 8 kHz or 44.1 kHz. As usedherein, the term “audio signal frame” may refer to a number ofconsecutive audio signal samples. In certain embodiments, a typicallength of time associated with an audio signal frame may be in a rangeof from 20 ms to 50 ms. For an audio signal frame of 20 ms at an 8 kHzsampling rate, and for an audio clip of one second, there are 1/20 ms=50frames, and for each frame there are 8000/50=40 samples.

FIG. 7A illustrates a first graphical user interface screen for a speechevaluation system. As shown, a user is prompted to read variousparagraphs of displayed text, either with or without feedback (e.g.,tactile, auditory, and/or visual feedback). Data including conditionsindicative of speech errors or other events may be recorded and/orplotted.

FIG. 7B illustrates a second graphical user interface screen for aspeech evaluation system, including upper and lower frames relating toan audio file generated by a user reading the displayed text shown inFIG. 7A. The upper frame of FIG. 7B graphically displays five eventssignifying actual or potential speech errors (identified by rectanglesoverlying the speech waveform). The lower frame of FIG. 7B enablesdisplay of additional information concerning speech analysis.

FIG. 8 illustrates superimposed third and fourth graphical userinterface screens for a speech evaluation system, including a backgroundframe prompting a user to read various paragraphs of displayed text(either with or without feedback) and including a superimposedforeground frame graphically displaying multiple events signifyingactual or potential speech errors (identified by narrow verticalrectangles or bars extending generally above the speech waveform).

The foregoing graphical user interface screens may be prepared usingMATLAB (MathWorks, Natick, Mass.) or another suitable software.

FIG. 9 is a perspective view illustration of a behind-the-necktherapeutic headset device 170 comprising audio input and stimuluscircuitry 178, and a band 176, according to one embodiment. The audioinput and stimulus circuitry 178 comprises a bone conduction microphone172 (that only picks up the voice of the wearer), and the audio inputand stimulus circuitry 178 comprises a vibrating element 174. The boneconduction microphone 172 may be arranged as a right capsule 172′ of thebehind-the-neck therapeutic headset device 170, and may be driven by aTS472 microphone amplifier (STMicroelectronics, Geneva, Switzerland).The vibrating element 174 may be arranged as a left capsule 174′ of thebehind-the-neck therapeutic headset device 170 and comprises a motor. Incertain embodiments, the band 176 of the behind-the-neck therapeuticheadset device 170 comprises a circuit board (e.g., with a wirelessmodule), a battery case, etc.

FIGS. 10A and 10B provide side elevation views of first and secondhalves 202A, 202B of a therapeutic behind-the-ear device 200. The firsthalf 202A may be mated with the complementary second half 202B (e.g.,with or without fasteners). The therapeutic behind-the-ear device 200further comprises at least one microphone 204, a processor (e.g., amicroprocessor) 206, a switch 208 (e.g., power switch), a vibratingelement 210, and/or a battery 212.

FIG. 11 is a diagram depicting an exemplary speech evaluation system 240providing an interface for a speech-language pathologist 232 via aclient device 234, wherein a patient 62 is a patient of thespeech-language pathologist 232. The client device 234 may be a PC, asmartphone, or a tablet device. The client device 234 provides thespeech-language pathologist 232 with a graphical administrator interface(GAI) portal 236, with the client device 234 optionally being remotelylocated from a network and server 224. In certain embodiments, the GAIportal 236 permits the speech-language pathologist 232 to monitor errorpatterns, communicate with the patient 62, and/or adjust a course oftreatment. In certain embodiments, the speech-language pathologist 232may be located in the presence of the therapeutic behind-the-ear device200 and/or interact with the patient 62 or the device 200 via a wiredinterface or close-proximity wireless interface (e.g., BLUETOOTH®(Bluetooth Sig, Inc., Kirkland, Wash., USA) or another wirelesscommunication protocol; not shown). In certain embodiments, the GAIportal 236 enables access to patient information and/or recordsindicative of problems and treatment. In certain embodiments, patientinformation comprises one or more of age, gender, patient identifier,device serial number, etc. In certain embodiments, the speech-languagepathologist 232 may select or alter operation of the therapeuticbehind-the-ear device 200 as part of a course of treatment of thepatient 62 to address a dysarthric condition. As shown, the therapeuticbehind-the-ear device 200 of FIGS. 10A and 10B is associated with thepatient 62, wherein the therapeutic behind-the-ear device 200 includesthe at least one microphone 204, the processor 206, the vibratingelement 210, and the battery 212. As shown, the therapeuticbehind-the-ear device 200 associated with the patient 62 is configuredto communicate with a network router 220 (e.g., optionally embodied in asmartphone or other communication-enabled computing device) that is incommunication with the client device 234 via the network and server 224,which may include the Internet or other desired wired and/or wirelessnetwork.

FIGS. 12A-12C are schematic diagrams of electronic circuitry 300A-300Museable with a speech evaluation device as disclosed herein. Generally,the electronic circuitry 300A-300M includes circuitry for power, analogsignal processing, control (e.g., for peripheral elements such asmotors, LEDs, etc.), communication, and/or debugging. Referring to FIG.12A, main circuitry 300A includes a microprocessor 302 (e.g., optionallyembodied in an ARM microcontroller with digital signal processingcapability and internal memory, such as a STM32F401 RB low profile quadflat pack (LQFP) microprocessor commercially available fromSTMicroelectronics (Geneva, Switzerland), although other types ofmicroprocessors could be used). As illustrated, the microprocessor 302includes 64 pins in electrical communication with one or more externalcircuits and/or circuit elements. In particular, the microprocessor 302is in electronic communication with: power-related circuitry 304-1 to304-8; clock circuitry 305 related to a microprocessor oscillator; resetcircuitry 306, and event circuity 308 related to event triggering (e.g.,which may be initiated via a button or other input device); power modecircuitry 310 related to power mode selection (e.g., to control activemode or standby mode of audio preprocessing circuitry); input circuitry312 related to analog input, such as from an audio preprocessor; motorcontrol circuitry 314 related to motor control (e.g., for providingvibratory or tactile stimulus to a user); clock circuitry 316 related toa clock (separate from the microprocessor clock), such as may be usefulto facilitate communication with circuit elements and/or other devices;master-in slave-out (MISO) circuitry 318 and master-out slave-in (MOSI)circuitry 320 to manage inter-element communications; LED controlcircuitry 322 to control activation of various LEDs to indicateoperating mode, to indicate operating status, and/or to facilitatesystem debugging; and debugging circuitry 324-1 to 324-3.

Referring to FIG. 12B, audio circuitry 300B includes an audio chip 326configured to pre-process an audio signal before it is transmitted tothe microprocessor 302 (shown in FIG. 12A; e.g., shift audio bias,increase amplitude, etc.). In particular, audio circuitry 300B includesaudio input circuitry 328 (shown in FIG. 12A; e.g., audio input jack),power mode selection circuitry 330, and debugging signal header 332.Mode selection circuitry 300C enables selection of a mode of themicroprocessor 302 (e.g., action, standby, etc.), and may providepinging functionality. Debugging circuitry 300D includes a debuggingheader 334. Communication circuitry 300E includes a communication header336 and manages communications with various circuit elements and/orother devices.

Referring to FIG. 12C, board power circuitry 300F provides powerconditioning and distribution for the circuit board. Audio powercircuitry 300G provides conditioned power for audio components. MCU(microcontroller unit) power circuitry 300H provides conditioned powerfor the MCU. MCU power indicator circuitry 3001 serves to indicate powerstatus for the MCU (e.g., using an LED). Event circuitry 300J providescircuit triggering functionality (e.g., employing one or more userinputs). MCU state circuitry 300K serves to indicate a state for the MCU(e.g., using an LED). Motor actuation circuitry 300L serves to controlactuation of at least one motor, which may provide vibratory or othertactile feedback to a user. Motor connection circuitry 300M facilitatesconnection and communication with a motor.

FIG. 13 is a layout diagram of hardware 402 incorporating at least aportion of the electronic circuitry of FIGS. 12A-12C. As shown, thehardware 402 includes a microprocessor 302, an audio chip 326, eventcircuitry 300J, audio power circuitry 300G, a microphone 404, a motor406, power input terminals 408, ground terminal 410, communicationcircuitry 300E, and debugging circuitry 300D. Of course, additional orfewer circuits relative to FIGS. 12A-12C may be included in the hardware402. Exemplary length and width dimensions of the hardware 402 are about40 mm by 40 mm. It is to be appreciated that FIGS. 12A-12C and FIG. 13are provided for purposes of illustration only, and that numerous otherimplementations may embody the structures and/or provide thefunctionality identified in the claims.

Upon reading the foregoing description in light of the accompanyingdrawing figures, those skilled in the art will understand the conceptsof the disclosure and will recognize applications of these concepts notparticularly addressed herein. Those skilled in the art will recognizeimprovements and modifications to the preferred embodiments of thepresent disclosure. All such improvements and modifications areconsidered within the scope of the concepts disclosed herein.

1.-10. (canceled)
 11. A system for evaluating speech, the systemcomprising processor circuitry configured to: receive a signalcomprising speech from an individual or representing speech from theindividual; extract a plurality of features useful for predicting arating scale-based evaluation of a neurological condition affectingspeech or language based on the signal; and generate, using a predictivemodel, the evaluation of the neurological condition, wherein thepredictive model is configured to generate the evaluation based on theplurality of features extracted from the signal.
 12. The system of claim11, wherein the processor circuitry is further configured to determine,based on said evaluation, at least one of disease onset, diseaseprogression, or disease treatment efficacy for the neurologicalcondition affecting speech or language.
 13. The system of claim 12,wherein the neurological condition comprises dysarthria as a symptom.14. The system of claim 11, wherein the system comprises a speechtherapeutic device comprising audio input circuitry and stimuluscircuitry, wherein the speech therapeutic device is configured toreceive the signal comprising speech from the individual and provide astimulus to the individual based on the evaluation of the rating scale.15. The system of claim 14, wherein the speech therapeutic devicecomprises a behind-the-ear device, an ear-mold device, a headset, aheadband, a smartphone, or a combination thereof.
 16. The system ofclaim 11, wherein the predictive model is calibrated based on aplurality of expert ratings corresponding to a plurality of patientspeech samples.
 17. The system of claim 11, wherein the evaluationcomprises a plurality of perceptual dimensions comprising two or more ofnasality, prosody, articulatory precision, vocal quality, or severity.18. The system of claim 11, wherein the plurality of features comprisesone or more of envelope modulation spectrum, long-term average spectrum,spatio-temporal features, or dysphonia features.
 19. The system of claim11, wherein the plurality of features useful for predicting the ratingscale has no more than 50 features.
 20. The system of claim 11, whereinthe evaluation comprises a multi-point evaluation.
 21. The system ofclaim 11, wherein the processor circuitry is further configured toprompt the individual to read displayed text prior to, or concurrentlywith, receipt of the signal comprising speech from the individual orrepresenting speech from the individual.
 22. The system of claim 11,wherein the plurality of features are selected fromlaboratory-implemented features using cross-validation andsparsity-based feature selection.
 23. The system of claim 11, furthercomprising a first graphical user interface configured to allow theindividual to provide the signal comprising speech from the individualor representing speech from the individual;
 24. The system of claim 23,further comprising a second graphical user interface configured topermit a speech-language pathologist or clinician to administer orreview the speech.
 25. A computer-implemented method for evaluatingspeech in a system involving processor circuitry, the method comprising:receiving, by the processor circuitry, a signal comprising speech froman individual or representing speech from the individual; extracting, bythe processor circuitry, a plurality of features useful for predicting arating scale-based evaluation of a neurological condition affectingspeech or language from the signal; and generating, by the processorcircuitry, the rating scale-based evaluation of the neurologicalcondition using a predictive model, wherein the predictive model isconfigured to generate the evaluation based on the plurality of featuresextracted from the signal.
 26. The method of claim 25, furthercomprising determining, based on said evaluation, at least one ofdisease onset, disease progression, or disease treatment efficacy forthe neurological condition affecting speech or language.
 27. The methodof claim 25, further comprising using a speech therapeutic device toreceive the signal comprising speech from the individual and provide astimulus to the individual based on the evaluation of the rating scale.28. The method of claim 25, wherein the predictive model is calibratedbased on a plurality of expert ratings corresponding to a plurality ofpatient speech samples.
 29. The method of claim 21, wherein theevaluation of the rating scale comprises a multi-point evaluation. 30.The method of claim 21, further comprising prompting the individual toread displayed text prior to, or concurrently with, receipt of thesignal comprising speech from the individual or representing speech fromthe individual.